{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import matplotlib.dates as mdates\n",
    "import time\n",
    "from scipy.optimize import minimize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.read_csv(\"bushu.csv\")  \n",
    "date = data['DATE']\n",
    "earning = data['EARNING']\n",
    "xs = [datetime.strptime(d, '%Y/%m/%d').date() for d in date]\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y/%m/%d'))\n",
    "plt.scatter(xs,earning,color='b')\n",
    "plt.gcf().autofmt_xdate()  # 自动旋转日期标记\n",
    "plt.ylabel(\"Earning\")\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "group = data['GROUP']\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(group,earning,color='b')\n",
    "plt.xlabel(\"Group\")\n",
    "plt.ylabel(\"Earning\")\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1af0453e518>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.optimize import curve_fit \n",
    "from scipy import asarray as ar,exp \n",
    "\n",
    "data1 = pd.read_csv(\"bushu1.csv\")  \n",
    "group1 = data1['GROUP']\n",
    "earning1 = data1['EARNING']\n",
    "x = group1\n",
    "y = earning1\n",
    "\n",
    "n = len(x)       #the number of data \n",
    "mean = sum(x*y)/n     #note this correction \n",
    "sigma = sum(y*(x-mean)**2)/n  #note this correction \n",
    "\n",
    "def gaus(x,a,x0,sigma): \n",
    "    return a*exp(-(x-x0)**2/(2*sigma**2)) \n",
    "\n",
    "popt,pcov = curve_fit(gaus,x,y,p0=[1,mean,sigma]) \n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.plot(x,y,'b',label='data') \n",
    "plt.plot(x,gaus(x,*popt),'r--',label='fit') \n",
    "plt.xlabel(\"Group\")\n",
    "plt.ylabel(\"Earning\")\n",
    "plt.legend() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = data['TIME']\n",
    "xs = [datetime.strptime(d, '%H:%M').time() for d in time]\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(xs,earning,color='b')\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Earning\")\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2 = pd.read_csv(\"bushu2.csv\")  \n",
    "time2 = data2['TIME']\n",
    "earning2 = data2['EARNING']\n",
    "\n",
    "def f_1(x, A, B):\n",
    "    return A*x + B\n",
    "\n",
    "A1, B1 = curve_fit(f_1, time2, earning2)[0]\n",
    "x1 = np.arange(0.2, 1.1, 0.01)\n",
    "y1 = A1*x1 + B1\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.scatter(time2,earning2,color='b')\n",
    "plt.plot(x1, y1, \"r\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Earning\")\n",
    "plt.show() "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
